Heft Nr. 29 Wuttke, E. (2000): Cognitive, Emotional and Motivational

Lehrstuhl für Wirtschaftspädagogik
Univ.-Prof. Dr. Klaus Beck · Univ.-Prof. Dr. Klaus Breuer
Fachbereich Rechts- und Wirtschaftswissenschaften
 Reihe: Arbeitspapiere WP 
29
Eveline Wuttke
Cognitive, Emotional and Motivational
Processes in an Open Learning
Environment – How to improve
vocational education
JOHANNES GUTENBERG-UNIVERSITÄT MAINZ
Herausgeber:
Lehrstuhl für Wirtschaftspädagogik
Univ.-Prof. Dr. Klaus Beck
Univ.-Prof. Dr. Klaus Breuer
Fachbereich 03: Rechts- und Wirtschaftswissenschaften
Johannes Gutenberg-Universität Mainz
Welderweg 9
D-55099 Mainz
Telefon: +49 6131 392-2009; Telefax: +49 6131 392-2095
email: beck@mail.uni-mainz.de
Wuttke, E. (2000): Cognitive, Emotional and Motivational Processes in an Open
Learning Environment – How to improve vocational education
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EVELINE WUTTKE
Cognitive, Emotional and Motivational Processes in
an Open Learning Environment – How to improve
vocational education1
1
Paper presented at AERA annual meeting, Tuesday April 25 2000, New Orleans. LA.
1.
Introduction
Adolescents today are faced with a rapidly changing, increasingly complex world.
Especially when they enter professional life, they have to cope with the fact, that the
knowledge they acquire today, might be obsolete before long. Therefore - with regard
to (future) job demands – they should be able to obtain certain basic skills during
vocational training, e.g.:
 The willingness to lifelong learning and the ability to do so Self-organized.
 Problem solving-competence, which will enable them to cope with complex situations during their training and in their future jobs.
In contrast to these demands, traditional learning at schools is usually prepared, organized and controlled by teachers and concentrates mainly on knowledge reproduction (SEMBILL/ WOLF/ WUTTKE/ SCHUMACHER 2000). The learning process and the students’ approaches to problems are often neglected. Thereby a central intellectual
competence is removed from the learning process: the use of judgement to recognize complex problems and the use of knowledge to solve them (BARROW 1993, 148).
We therefore need a reengineering process of classroom teaching with powerful
learning environments (as described by BEREITER and SCARDEMELIA 1989, BROWN/
COLLINS/ DUGUID 1989 and de CORTE 1995, 72) and compatible didactical methods.
The question is, how such new ways of learning can be initiated – learning that includes problem solving and supports the motivation to keep on learning.
2.
A new way of learning in a rapidly changing world
New ways of learning can be achieved, when students are given the opportunity to
learn Self-organized, to deal with complex problems, to organize their own learning
process and to assume responsibility for the results (SEMBILL 1995, 131). Such processes have to be supported by adequate learning environments, that are open for
students activities and based on complex real-world problems. The concept of Selforganized learning1 in an open learning environment is based on four dimensions,
1
This work has been supported in part by a grant from the Deutsche Forschungsgemeinschaft (DFG
- AZ.: SE 573/4-1/ 4-2)
that are to be seen as basic requirements for the implementation of a learning environment2 (SEMBILL 1992a, 75):
Learning for oneself: The learning environment has to be created in a way, that students have to reflect about the sense and purpose of own learning processes and their personal responsibility for general values.
Learning with others: Includes the requirement to assess the relevance of given
learning contents for oneself and communicating this relevance
actively in lateral co-operation processes with fellow learners.
Learning for others: Through externalization of cognitive, emotional and motivational
skills during the learning process, students can profit from each
other.
Learning with risk:
Learning processes involve complex problem solving activities,
that include the possibility of mistakes and inadequate ways of
problem solving. If necessary, teachers coach the learning process and advise students about problem solving strategies3.
Learning by solving complex, ill-defined problems means that students:
•
have to define the problem (analyze given situations and specify goals),
•
use their pre-knowledge,
•
collect necessary information ,
•
propose solutions,
•
analyze side- and subsequent effects in relation to the main effects,
•
realize proposed solutions and
•
control the learning outcome.
2
For a more detailed description of the basic assumptions of the underlying learning theory of Selforganized learning see SEMBILL/ WOLF/ WUTTKE/ SCHUMACHER 2000.
3
These characteristics show a strong similarity to the way learning is organized when based on the
concept of anchored instruction (Cognition and Technology Group at Vanderbilt 1997) or situated
learning/cognition (RESNICK 1987; BROWN/ COLLINS/ DUGUID 1989). They have in common that
learning processes are based on complex, real-world problems (authentic activities). Facts, that
help solving the problem, must be collected (facts hidden in the material students receive and facts
they have to collect externally). Students have to reach a solution for the problem. An active generation of knowledge structures and problem solving strategies is encouraged which will enable
students to transfer their abilities to other problems/ tasks.
2
These basic requirements are reflected in the concrete realization of our learning environment (didactic design) with industrial clerks in training. Contents were situated in
"materials administration", which is a central part of their curriculum.
The didactic design was organized in five steps during which students acquire their
skills (WUTTKE 1996, WUTTKE 1999), 154):
1. Definition of objectives:
Before the learning process actually started, teachers and students defined cooperatively the contents to be learned and objectives that should be reached4. Rights
and duties were specified.
2. Self-organized learning – Phase I:
The first phase of Self-organized learning can be characterized by a reduced complexity to give students the chance to adapt to Self-organized learning and to get
used to problem solving activities. The students worked in groups on the same problem (see Fig. 1).
Young Travels
Post Box 112
35394 Gießen
Max-Weber-Büromöbelfabrik
Post Box 45
35357 Gießen
September 21, 1994
Office equipment
Ladies and Gentlemen,
we have learned from the trade journal "Business Table", that you are offering complete solutions for
office equipment. We are part of a chain of travel agencies for young people, and we plan to open a
subsidiary in the student village in Gießen. We will specialize in three kinds of travels:
1. Organized travels to foreign countries for young people
2. Single meets single all over the world
3. Last-minute travels
We have already purchased rooms in Gießen. In our main room (about 72 m2) we would like to have
four workplaces. One should be a kind of reception area where we can find out about clients interests.
At the remaining workplaces clients will be able to get information according to our three kinds of travels
and to book their journeys.
4
This process is partly restricted due to curriculum requirements.
3
For every workplace we need a desk, a desk-chair, lights and a filing cabinet. Furthermore there should
be bookcases and shelves for our catalogues. Apart from the furniture, we will need telephones for all
workplaces, a fax-machine and four PCs with printers and modems. Standard software (Windows)
should be provided as well. Our parent branch will deliver the special software for travel agencies.
We are looking forward to your offer.
With friendly regards
Young Travels
Fig. 1: Problem to be solved in the first phase of Self-organized learning
Even with reduced complexity, all components of a complex problem were given.
Students had to analyze the given problem, collect information, develop and realize
solutions and evaluate the results.
First presentation and evaluation:
Because groups have different approaches to problems and probably reach different
results, an integral part of such a learning environment is the presentation of findings
and solutions. Therefore, after the first phase of Self-organized learning, group presentations took place. All results were discussed and, if necessary, the teacher provided additional information.
Self-organized learning – Phase II:
This phase is characterized by an increased amount of complexity, which is partly the
result of groups working on different problems.
Second presentation and evaluation:
The second presentation was even more important, because the groups had worked
on different problems with different contents. All contents are part of the curriculum
and relevant for their exams. Therefore teachers have to guarantee, that students
deal with all aspects of the problem and that all parts of the curriculum are sufficiently
taken into account.
Learning environments based on complex real-world problems can only be successful on condition that enough information for adequate solutions is provided. In traditional learning processes teachers are the source of information. In Self-organized
learning processes other sources of information are to be provided. In our study, 'traditional' sources of information (books, trade journals, yellow pages etc.) were avail-
4
able in the classroom. Furthermore, we implemented a computer mediated learning
environment (WOLF 1995; 1996).
This learning environment enables students to:
•
create their own documents and construct links between documents without further knowledge of the underlying Hypertext Markup Language (HTML);
•
communicate with each other:
•
co-operate and collaborate on their work/ learning.
Fig. 2:
Overview of the 2nd generation web based learning environment 1994-95 (SoLe/W3)5
1. Inside the media center students can either access hyper-structured media (reading room) or create their own media (editor's office). The media consist of text-,
audio- and video-documents. A very important feature of the system is the easy
creation of own content by students. A link to the newly made documents is
automatically inserted into the register of student-created documents and into the
expo of the student group. Enabling students to create and structure their own
knowledge space supports learning in a more holistic, networked way.
5
More information about a third generation learning community server can be found at
http://www.eduserf.de) (WOLF 1999, WOLF 2000).
5
2. Self-organized learning stands for learning for oneself, with others and for others
(see page 2). It also means presenting own learning results to fellow students and
teachers to get feedback. In the expo, students' pieces of work are collected and
displayed in group- or single-rooms. Students can visit the exposition and study
the creations of their peers.
3. In the communication center students find:
•
a blackboard, where they can display information of general interest;
•
discussion rooms, where they can talk about specific problems. It is also possible, to create own discussion rooms;
•
a post office, where they can send mail to other students, the teacher and the
research group.
•
a mailbox, where they can give feedback about the system to the webmaster;
4. The corporation was especially implemented for the materials administration
course of our participants. The teachers had created a description of an office furniture manufacturer in toolbook, a hypermedia authoring system with several Excel- and Word-files describing inventories etc. In the corporation, student groups
can start their copy of this external hypermedia-document and work with Excel
and Word.
3. How to define successful learning and why the learning process should be
analyzed
The benefits of learning in a complex authentic context have been suggested by
many sources from DEWEY (1938) to the recent discussions of for example Selforganized learning, anchored instruction, situated learning and situated cognition.
Context provides meaning, enriches perception and affords development of complex
problem solving and higher level thinking skills. If this is seen as a fact, we must develop means to assess the effects of such learning environments (YOUNG/ KULIKOWICH/
BARAB 1992). It certainly won't be enough to assess the amount of knowledge
reproduction and call this "successful learning". If, for example, problem solving competence is stated as central objective of learning processes, problem solving competence should be evaluated.
6
Our evaluation of problem solving competence is based on four steps that have been
identified as integral parts of complex problems by problem solving research
(DÖRNER 1976; DÖRNER 1983; SEMBILL 1992a):
1. Analysis of the given situation;
2. Specification of goals;
3. Development of adequate measures to solve the problem;
4. Mental control, if the solutions are adequate and the problem is solved.
After the learning process, students were given problem descriptions and they were
asked to produce written solutions. In a first approach, these solutions were evaluated quantitatively (do students mention all four steps of problem solving, how many
measures do they propose etc.). Then the categories were added up to a score
called "Analytic Ideal Type" (AIT, see SEMBILL 1992b; WUTTKE/ SANTJER 1996). The
higher this score is, the more successful students are in problem solving. The second
step was an analysis of quality (do the students offer correct solutions, can it be expected that solutions are successful in a real-life problem, do students consider side
and subsequent effects in addition to the main effect etc.). The method of this part of
the evaluation is an expert rating.
Apart from having adequate criteria to assess successful learning, it is important to
analyze the learning process itself. By doing this, relevant components of the learning process and their influence on the learning outcome can be identified. For that
reason we registered self-reported emotional, motivational and cognitive state variables of learners in five-minute intervals (see Fig. 4). With the help of this information, substantial evidence for successful learning can be found. The focus of our
study was:
 To investigate, whether a learning environment, that is open to Self-organized
learning, supports successful learning.
 To find out, how these effects can be described in detail and how they might be
explained.
7
4.
Method
4.1 Design Overview
We realized and evaluated Self-organized learning together with teachers of a vocational school. During two increasingly complex phases (40 hours), students had to
deal with problems situated in materials administration (as an example see Fig. 1),
which is an important part of their vocational education. For comparison, the same
contents were taught by the same teacher in a control-class under rather traditional
conditions (teacher centered).
4.2 Participants
The participants were future industrial clerks in their second year of education in the
German dual system. They are at vocational school for 1 ½ days per week, the remaining part of the week they are working/learning in companies. In the experimental
class we had 21 participants, in the control class there were 14 participants. There
was a definite advantage of the control class: they were better educated, with higher
intelligence and a better pre-knowledge (for details see WUTTKE 1999, 190).
4.3
Procedure
The methodological design followed a product-process-product approach (Fig. 3). In
both classes we collected data before ("Start" = t0) and a after ("End" = t1) the treatment about students' motivation, their learning strategies, their prior knowledge (t0)
and their knowledge and problem solving competence (t1). To evaluate problem solving, students were confronted with two problem descriptions, to which they were
asked to find solutions (see chapter 3). Apart from the treatment or learning environment (Self-organized learning vs. traditional learning) both classes learnt under the
same conditions (same teacher, same contents).
Fig. 3 Methodological design. SoLe = Self-organized learning; TraLe = traditional learning.
8
In the process we acquired data from five different sources (Fig. 4).
Fig. 4: Sources of process data
Most important (in the center) were self-reported emotional, motivational and cognitive state variables of learners, that were registered in five-minute intervals by a 4scaled rating in a mobile mini-computer6:
Emotion:
“Others take me seriously” (S)
“I feel good” (G)
Cognition:
“I understand” (U)
Motivation:
“I am interested” (I)
“I can participate actively” (P)
These data provide the basis for a differentiated analysis of the interrelation of subjective experience, behavior and influence of the learning environment.
5.
Results
5.1 Product data
As criteria for successful learning we chose two measures: One was a standard test
to assess learning, composed by teachers. This test requires the reproduction of
knowledge acquired during the learning process. We expected, that both classes
would score similarly in this test. An advantage of the experimental class was not
expected, because Self-organized learning does not focus particularly on knowledge
6
For the analysis of video data see PFEIFFER 1998, BRÄUER 1999, GLOMBIG 1999, WUTTKE 1999
9
reproduction. Nevertheless, it must be guaranteed, that the experimental class is
able to pass this test as well as the control class, because such tests are one part of
their final exam. To assess problem solving competence, students were given two
problem descriptions, to which they had to produce a written solution. These solutions were analyzed concerning their completeness and quality (see chapter 3).
Concerning the test to assess knowledge reproduction we could confirm our assumption: no difference between experimental and control class could be found (Fig. 5) - in
both classes students were quite successful.
SoLe (n=21)
M
(S)
6.86
2.06
Knowledge materials administration
TraLe (n=14)
M
(S)
6.64
2.62
t
p
.271
.788
Fig. 5: Knowledge Differences between experimental (SoLe = Self-organized learning) and control
class (TraLe = traditional learning) in materials administration (t-test).
But if we take into account that the control class started with a considerably higher
pre-knowledge (mean = 5.21 compared with 3.21 in the experimental class) this can
nevertheless be seen as quite a success for the experimental class (see Fig. 6).
K n o w le d g e in m a te ria l a d m in is tra tio n b e fo re
a n d a fte r th e tre a tm e n t
7 ,5
7 ,0
6 ,5
6 ,0
5 ,5
T yp es of
T re a tm e n t
5 ,0
4 ,5
SoLe
4 ,0
T ra L e
3 ,5
1
2
tim e
Fig. 6: Knowledge in materials administration before and after the treatment. SoLe = experimental
class, TraLe = control class.
If we analyze problem solving competence we find a definite advantage in favor of
the experimental class.
10
Materials administration
SoLe
M
TraLe
(S)
M
t
P/ω
2
(S)
Formal/ quantitative categories
Analysis of given situation
9
3,86
9
4.35
.000
1.000
Definition of goals
1.29
1.95
.86
1.03
.199
.752
Measures
1.71
1.49
.86
.95
1.907
.065/0.07
Control
1.81
1.47
0.50
0.46
3.45
0.20
6.8533
3.5361
4.6707
3.1044
1.876
.070/0.07
AIT
Qualitative categories
Declarative knowledge
3.43
0.75
2.79
0.80
2.42
0.21
Knowledge network
3.57
1.83
2.50
1.34
1.874
0.70/0.07
Logic
3.00
1.30
2.07
1.27
2.086
.045
Potential success
2.67
1.32
1.71
.99
2.300
0.28
Fig. 7:
Categories of problem solving competence. SoLe = experimental class, TraLe = control
class. ω2 is a measure for practical significance. 7 % (and more) are generally seen as being
acceptable (BREDENKAMP 1972, 49).
The students, who learnt Self-organized, deliver more complete solutions (formal
categories) and a better quality of solutions (qualitative categories). A (statistically or
practically) significant difference in favor of Self-organized learning can be found in
the categories 'measures', 'control', 'AIT', 'declarative knowledge', 'knowledge networks', 'logic' and 'potential success'. These results indicate, that our open learning
environment supports successful learning and problem solving.
The following results are concentrated on the second focus of our study: to find out
how the effects shown above can be described in detail and how they might be explained.
5.2 Process data
As mentioned above, we recorded in five-minute-intervals five cognitive, emotional
and motivational state variables (see Fig. 4). With these data, time series can be created, that represent individual or aggregated processes (class level). On the one
hand, aggregation leads to a certain loss of information, on the other hand, it can be
helpful as a first step to detect general processes.
11
The time series indicate, that the processes in the experimental class (Self-organized
learning) were in some categories more favorable than those in the control class
(class level). The following graphic shows (as an example) the series of the item "I
can participate actively":
SoLe – I can participate actively:
S Participate By t
S
Mit
ge
sta
lte
n
3
2
0
50
100
150
200
150
200
t
TraLe – I can participate actively:
T Participate By t
3,2
3,1
3,0
2,9
2,8
T
Mit 2,7
ge
sta 2,6
lte 2,5
n
2,4
2,3
2,2
2,1
2,0
1,9
1,8
0
50
100
t
Fig. 8: Aggregated time series of the item "I can participate actively" in the experimental class
(above) and control class (below).
In the experimental class we can clearly observe the phases of the didactic design. In
the first phase (left circle), students have the feeling that they can participate actively.
This was a period of time, when they had to reflect about the previous working period
12
by themselves. After this, we find a certain decrease in the realization of possible
participation, because the teacher provided feedback and recollected the previous
working process (circle below). Then there is again a period of time, when students
experienced active participation. This is a new period of Self-organized learning or
problem solving (ellipse). The decrease towards the end (right circle) can be explained by the didactic design as well: the groups presented their results of the previous problem solving process. During this time, other students' participation possibilities were restricted.7
The maximum in the control class is, apart from two exemptions, constantly below
3.0, whereas the experimental class reaches a mean of 3.1 during the second phase
of Self-organized problem solving (ellipse). Maximum values are 3.4. In the control
class we notice a peak around t = 113. This period has to be analyzed with the help
of our video data, concerning contents and methods, that might have led to such a
peak. In general, such time series provide hints for potentially interesting phases in
learning processes. These have to be analyzed in a second step with the help of
video data.
Apart from descriptive analysis we compared the means of the two classes (Fig. 9).
Mean SoLe
mean TraLe
Std.dev. SoLe
Std.dev. TraLe
Others take me
seriously
2,7157
2,7921
0,1675
0,1517
I feel good
2,6687
2,8141 *
0,1907
0,1984
I understand
3,0424
2,9267
0,1643
0,1766
I am interested
I can participate
actively
2,8377 *
2,7463 *
2,5849
2,4655
0,1980
0,4078
0,1809
0,2362
Fig. 9: means and standard deviations of aggregated time series in the experimental (SoLe) and
control class (TraLe). * = significant on the level α = 5%).
These results show, that students, who learnt Self-organized, were convinced to
have more possibilities to participate actively, and they were significantly more interested. The understanding of the learning content was slightly higher in this class as
well. However, the emotional items show a slight advantage in favor of the class that
7
We also analyzed time series of subgroups or individuals. These give additional information about
learning processes. For example, we found, that the class, that learnt Self-organized was very heterogeneous. Apparently some students can cope better with such a way of learning than others do.
Teachers should be aware of this and offer individual help (see WUTTKE 1999).
13
learnt traditionally. This is probably due to the complexity and uncertainty of the
learning situation in the Self-organized class. Some students might have difficulties in
coping. If subgroups of this class are analyzed this can be confirmed – there is a significant difference concerning emotional variables between two subgroups of the experimental class (WUTTKE 1999). Similar differences between these subgroups can
be detected concerning motivational variables and the use of learning strategies
(WUTTKE 1999).
To get more information about the learning processes in the two classes, we analyzed if and how these process variables are connected. For that purpose, crosscorrelations of the time series have to be computed (SEMBILL/ WOLF/ WUTTKE/ SANTJER/
SCHUMACHER 1998). We analyzed synchronous and asynchronous (with a time
lag of 5 and 10 minutes) cross-correlations between the variables. Especially asynchronous cross-correlations are interesting, because only those allow statements
about how one variable affects or influences the other.
TraLe (t)
TraLe(t + 5 min.)
I feel good
.28
Others take me seriously
.31
.21
.38
I understand
I understand
.23
.49
.16
.46
.17
I am interested
.49
I can participate
.33
.15
.36
.15
I can participate actively
14
TraLe (t + 10 min.)
SoLe (t)
I feel good
I feel good
.15
.42
.16
Others take me seriously
.28
SoLe (t + 10 min)
SoLe ( t + 5 min)
.17
.30
I understand
Others take
me seriously
Others take
me seriously
.22
.18
.18
I understand
.20
.40
.46
.42
I am interested
.17
.27
.25
.33
I can participate actively
I can participate actively
Fig. 10: Cross-correlations between the process data in the control class (above) and the experimental class (below)
In both classes all five items are closely connected (t; synchronous correlation). This
result indicates, that cognitive, emotional and motivational processes during the
learning process cannot be seen as being separate.
In both classes we find an influence of the emotional item "I feel good" and the motivational item "I am interested" (both in t) on the item "I understand" (t + 5 min.). In the
experimental class there is an additional influence of "others take me seriously"
(emotional item) on the understanding of the learning contents. These seem to be
basic processes, that are fairly independent of a specific teaching method: if students
feel good, if they are interested and integrated in their learning group (others take me
seriously), learning and understanding will be enhanced.
Apart from these basic processes, we find rather different cross-correlations in the
two classes. A remarkable result is, that the processes in the experimental class
seem to be more lasting than in the control class and that more variables are connected over a longer period of time. This is a positive result if students feel good, are
interested and well integrated in their peer-group. But it can be fatal, if emotional and
motivational variables are negative. Therefore, before implementing Self-organized
learning, teachers should know about the possibility of such processes. They should
be able to identify students who have problems in coping with Self-organized learning
and to offer adequate help.
15
The next step was to analyze the correlation between our process data (5 variables)
and the criteria of success. We could not find any significant direct connection. But it
is possible to show an indirect influence of the five process items on problem solving
competence via students' motivation and their learning strategies (for detail see
WUTTKE 1999; WUTTKE 2000).
6. Concluding Comments
In view of changing qualification profiles for adolescents entering professional life, we
tried to implement a learning environment in which students learn Self-organized.
The learning environment was based on complex, authentic problems.
The first focus of the study was to show, that Self-organized learning supports successful learning. The data indicate, that a Self-organized learning process can enhance problem solving without neglecting students knowledge of necessary facts.
With the analysis of our process data (second focus) new insights are possible:
When time series of the two classes are compared, it can be shown, that Selforganized learners can participate more actively and are therefore more interested in
the contents. We could also show, that in both classes cognitive, emotional and motivational processes are closely connected. In both classes students have a higher
understanding of the learning content when they are feeling good, are interested and
well integrated in their learning group. The difference between the two classes can
be seen in the closer connection of the variables in the experimental class and in the
fact, that the correlations can be found over a longer period of time. This result can
be positive or rather dangerous, depending on how students are able to cope with a
Self-organized learning environment.
In general, Self-organized learning can be seen as a powerful and constructive way
to reach higher qualification and to deepen students' interest in and understanding of
learning contents. But even if the arrangement is successful in general, some students seem to have difficulties in coping with complexity and uncertainty. The conclusion cannot be to go back to traditional teaching. But these results must lead to
didactical consequences, e.g.:
¾ Teachers must be able to identify students, who have difficulties with such a
learning arrangement.
16
¾ Furthermore, they should be able to help students to acquire necessary tools to
learn Self-organized (e.g. learning strategies, problem solving strategies).
In addition, these consequences must lead to new ways of teacher education (or further education), because presently teachers might neither be able to identify such
problems nor to help with adequate tools to cope with them.
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BREDENKAMP, J. (1972): Der Signifikanztest in der psychologischen Forschung. (Akademische
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18
UNIT OF ANALYSIS FOR SITUATED ASSESS-
Bisher erschienen
Heft Nr. 1
Lüdecke-Plümer, S., Zirkel, A. & Beck, K.
(1997). Vocational Training and Moral Judge-
ment – Are There Gender-Specific Traits
Among Apprentices in Commercial Business?
Heft Nr. 2
Minnameier, G., Heinrichs, K., Parche-Kawik,
K. & Beck, K. (1997). Homogeneity of Moral
Judgement? - Apprentices Solving Business
Conflicts.
Heft Nr. 3
Lüdecke-Plümer, S. (1997). Bedingungen der
Entwicklung des moralischen Urteils bei angehenden Versicherungskaufleuten.
Heft Nr. 4
Heinrichs, K. (1997). Die „Schlüsselqualifika-
tion“ moralische Urteilsfähigkeit – Ihre Entwicklung und Ausprägung bei kaufmännischen
Auszubildenden.
Heft Nr. 5
Beck, K. (1997). The Segmentation of Moral
Judgment of Adolescent Students in Germany
– Findings and Problems.
Heft Nr. 6
Heinrichs, K. (1997). Betriebsbezogene morali-
sche Urteile von Auszubildenden im Versicherungswesen – Persönlichkeitsspezifisch oder
situationsspezifisch?
Heft Nr. 7
Sczesny, Ch. (1997). Das Lösungsverhalten bei
wirtschaftskundlichen Aufgaben – Visuelle und
physiologische Begleitprozesse situierter kognitiver Leistungen.
Heft Nr. 8
Beck, K., Bienengräber, Th., Heinrichs, K.,
Lang, B., Lüdecke-Plümer, S., Minnameier, G.,
Parche-Kawik, K. & Zirkel, A. (1997). Die mo-
ralische Urteils- und Handlungskompetenz von
kaufmännischen Lehrlingen – Entwicklungsbedingungen und ihre pädagogische Gestaltung.
Heft Nr. 9
Beck, K. (1997). The Development of Moral
Reasoning During Vocational Education.
Heft Nr. 10
Sczesny, Ch., Lüdecke-Plümer, S. (1998). Öko-
nomische Bildung Jugendlicher auf dem Prüfstand: Diagnose und Defizite.
Heft Nr. 11
Lüdecke-Plümer, S., Sczesny, Ch. (1998). Öko-
nomische Bildung im internationalen Vergleich.
Heft Nr. 12
Beck, K., Bienengräber T., Parche-Kawik, K.
(1998). Entwicklungsbedingungen kaufmänni-
scher Berufsmoral – Betrieb und Berufsschule
als
Sozialisationsmilieu
für
die
Urteilskompetenz.
Heft Nr. 13
Beck, K. (1998). Wirtschaftserziehung und
Moralerziehung – ein Widerspruch in sich? Zur
Kritik der Kohlbergschen Moralentwicklungstheorie.
Heft Nr. 14
Beck, K. (1998).
Berufsausbildung?
Moralerziehung
in
der
Heft Nr. 15
Beck, K. (1998). Ethische Differenzierung als
Grundlage, Aufgabe und Movens Lebenslangen
Lernens.
Heft Nr. 16
Parche-Kawik, K. (1998). Wirtschaftsethik und
Berufsmoral – Die Auseinandersetzung um das
Leitbild für den modernen Kaufmann im Lichte
empirischer Befunde.
Heft Nr. 17
Hillen, S./ Berendes, K./ Breuer, K. (1998):
Systemdynamische Modellbildung als Werkzeug zur Visualisierung, Modellierung und Diagnose von Wissensstrukturen.
Heft Nr. 18
Breuer,
K./
Die
Höhn,
K.
(1998):
Implementation
eines
Qualitätsförderungssystems für be-rufliche
Abschlußprüfungen – Eine Interventionsstudie
am
Beispiel
des
neu
geordneten
Ausbildungsberufs
Versicherungskaufmann/
Versicherungskauffrau.
Heft Nr. 19
Beck, K. (1998): Die Entwicklung moralischer
Urteilskompetenz in der kaufmännischen Erstausbildung – Zur Analyse der Segmentierungshypothese.
Minnameier, K. (1998): Homogenität versus
Heterogenität des moralischen Denkens – Wie
urteilen
Auszubildende
im
Rahmen
betrieblicher Kontexte?
Zirkel,
A.
(1998):
Kaufmännische
Berufsausbildung und moralisches Denken –
Erleben
weibliche
und
männliche
Auszubildende
ihre
soziale
Umwelt
unterschiedlich?
Heft Nr. 20
Wuttke, E. (1999): Lernstrategieeinsatz im
Lernprozeß und dessen Einfluß auf den
Lernerfolg.
Heft Nr. 21
Beck, K. (1999): Zur Lage der Lehr-Lern-
Forschung - Defizite, Erfolge, Desiderate.
Heft Nr. 22
Minnameier,
G. (1999): Wie man lernt,
komplex zu denken, oder: Was substantielle
Lernfortschritte sind und wie man sie erzielt.
Heft Nr. 23
Beck, K. (1999): Fortschritt in
Erziehungswissenschaft? Anmerkungen
Gegenwart einer unendlichen Geschichte.
der
zur
Heft Nr. 24
Bienengräber, Th. (1999): Die Entwicklung
moralischer Urteilskompetenz im Betrieb – zur
Bedeutsamkeit einzelner sozialer Bedingungen
für
die
Genese
der
moralischen
Urteilsfähigkeit.
Heft Nr. 25
Parche-Kawik,
K. (1999): Wahrnehmung
sozialer
Interaktionsbedingungen
am
Arbeitsplatz – Unterschiede in den Sichtweisen
von Auszubildenden und ihren Ausbildern.
Heft Nr. 26
Beck, K. (2000): Die Moral von Kaufleuten –
Über
Urteilsleistungen
und
Beeinflussung durch Berufsbildung.
deren
Heft Nr. 27
Bienengräber, Th. (2000): Zur Frage der
Bereichsspezifität in der Wirkung moralischer
Entwicklungsbedingungen.
Heft Nr. 28
Beck, K. (2000): Alternative Research Ap-
proaches: Development Strategies in Educational Technology.